本文提出了一种求解实值数值优化问题的加权微分进化算法（WDE）。

In this paper, Weighted Differential Evolution Algorithm (WDE) has been proposed for solving real valued numerical optimization problems.

当WDE的所有参数都是随机确定时，实际上，WDE没有控制参数，只有模式大小。

When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. 

WDE可以解决单模、多模、可分离、可扩展和混合问题。

WDE can solve unimodal, multimodal, separable, scalable and hybrid problems. 

WDE具有快速、简单的结构，而且由于其非递归性，可以实现并行化。

WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its nonrecursive nature.

WDE具有很强的探索研究能力。

WDE has a strong exploration and exploitation capability.

本文将WDE在解决CEC'2013问题方面的成功与4种不同的EAs（即CS、ABC、JADE和BSA）进行了统计比较。

In this paper, WDE’s success in solving CEC'2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. 

利用三维几何优化问题（即GPS网平差问题）和4个约束工程设计问题，检验了WDE解决实际问题的能力。

One 3D geometric optimization problem (i.e., GPS Network Adjustment Problem) and 4 constrained engineering design problems were used to examine the WDE’s ability to solve real-world problems. 

实验结果表明，一般而言，在统计学意义上，本文所用的WDE求解成功率高于其它比较算法。

Results obtained from the performed tests showed that, in general, problem solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

参考文献：

P Civicioglu, E Besdok, MA Gunen, UH Atasever, (2018), Weighted Differential Evolution Algorithm for Numerical Function Optimization ; A Comparative Study with Cuckoo Search, Artificial Bee Colony, Adaptive Differential Evolution, and Backtracking Search Optimization Algorithms, Neural Comput & Applic (2018). https://doi.org/10.1007/s00521-018-3822-5

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